feat: 2026-04-15~04-20 累积变更基线 — 多主线合流
主线 1: rns1-customer-coach-api + 04-miniapp-core-business 后端实施
- 新增 GET /xcx/coaches/{id}/banner 轻量接口
- performance/records 加 coach_id 参数 + view_board_coach 权限分流
- coach/customer/performance/board/task 服务层重构
- fdw_queries 结算单粒度聚合 + consumption_summary 视图统一
- task_generator 回访宽限 72h + UPSERT 替代策略 + Step 5 保底清理
- recall_detector settle_type=3 双重限制 + 门店级 resolved
主线 2: 小程序权限分流 + 新增 coach-service-records 管理者视角业绩明细页
- perf-progress 共享模块去重 task-list/coach-detail 动画逻辑
- isScattered 散客标记端到端
- foodDetail/phoneFull/creator* 字段透传
主线 3: P19 指数回测框架 Phase 1+2
- 3 个指数表 stat_date 日快照模式
- 新增 DWS_INDEX_BACKFILL / DWS_TASK_SIMULATION 工具任务
- task_engine 升级 HTTP 实时 + 推演回测双模式
主线 4: Core 维度层启用
- 新增 CORE_DIM_SYNC 任务（DWD → core 4 维度表）
- 修复 app 视图空查询问题
主线 5: member_project_tag 改为 LAST_30_VISITS 消费次数窗口
主线 6: 2 个迁移 SQL 已执行（stat_date + member_project_tag 新窗口）
- schema 基线与 DDL 快照同步
主线 7: 开发机路径迁移 C:\NeoZQYY → C:\Project\NeoZQYY（约 95% 改动量）
附带: 新建运维脚本（churned_customer_report / simulate_historical_tasks /
backfill_index_snapshots）+ tools/task-analysis/ 任务分析工具
合计 157 文件。未包含中间产物（tmp/ .playwright-mcp/ inspect-* excel/sheet 分析 txt）。
审计记录见下一个 commit。
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
390
tools/task-analysis/gen_task_report.py
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390
tools/task-analysis/gen_task_report.py
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# -*- coding: utf-8 -*-
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"""
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助教业务任务明细报告生成工具
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分组结构：指标说明 → 总览 → 按助教分组（内按任务类型分组）→ 趋势分析
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用法：
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python tools/task-analysis/gen_task_report.py
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python tools/task-analysis/gen_task_report.py --output docs/custom-report.md
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python tools/task-analysis/gen_task_report.py --status active,completed
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"""
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from __future__ import annotations
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import argparse
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import os
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import sys
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from datetime import date
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from collections import defaultdict, OrderedDict
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from pathlib import Path
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import psycopg2
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_TOOL_DIR = Path(__file__).resolve().parent
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_ROOT = _TOOL_DIR.parents[1]
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from dotenv import load_dotenv
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load_dotenv(_ROOT / ".env", override=False)
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load_dotenv(_ROOT / "apps" / "etl" / "connectors" / "feiqiu" / ".env", override=False)
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# ── 常量 ──
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TYPE_ORDER = ["high_priority_recall", "priority_recall", "follow_up_visit", "relationship_building"]
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TYPE_LABELS = {
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"high_priority_recall": "高优先召回",
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"priority_recall": "优先召回",
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"follow_up_visit": "客户回访",
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"relationship_building": "关系构建",
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}
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STATUS_LABELS = {
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"active": "进行中", "completed": "已完成",
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"resolved": "已解除", "expired": "已过期", "inactive": "已关闭",
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}
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# 排序权重：已完成 -> 进行中 -> 已解除 -> 已过期 -> 已关闭
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STATUS_SORT = {"completed": 0, "active": 1, "resolved": 2, "expired": 3, "inactive": 4}
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def fmt(v, digits=2):
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if v is None:
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return "-"
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return f"{float(v):.{digits}f}"
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def fmt_mobile(m):
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if not m:
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return "-"
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return str(m)
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# ── 数据加载 ──
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def load_etl_data(dsn: str) -> dict:
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conn = psycopg2.connect(dsn)
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conn.set_client_encoding("UTF8")
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cur = conn.cursor()
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# 助教（昵称 + 离职）
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cur.execute(
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"SELECT assistant_id, nickname, COALESCE(leave_status, 0) "
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"FROM dwd.dim_assistant WHERE scd2_is_current = 1"
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)
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asst_names = {}
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asst_left = set()
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for r in cur.fetchall():
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asst_names[r[0]] = r[1]
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if r[2] == 1:
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asst_left.add(r[0])
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# 客户昵称 + 手机号
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cur.execute("SELECT member_id, nickname, mobile FROM dwd.dim_member WHERE scd2_is_current = 1")
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member_names = {}
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member_mobiles = {}
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for r in cur.fetchall():
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member_names[r[0]] = r[1]
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member_mobiles[r[0]] = r[2]
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# relation_index（最新快照）
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cur.execute("""
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SELECT DISTINCT ON (assistant_id, member_id)
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assistant_id, member_id, rs_display, os_label,
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session_count, days_since_last_session, ms_display, ml_display
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FROM dws.dws_member_assistant_relation_index
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ORDER BY assistant_id, member_id, stat_date DESC
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""")
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ri_map = {}
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for r in cur.fetchall():
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ri_map[(r[0], r[1])] = {
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"rs": r[2], "os_label": r[3],
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"sessions": r[4], "days_since": r[5], "ms": r[6], "ml": r[7],
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}
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# WBI（含 status 用于判断客户是否已转老客）
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cur.execute("""
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SELECT DISTINCT ON (member_id) member_id, display_score, status
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FROM dws.dws_member_winback_index ORDER BY member_id, stat_date DESC
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""")
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wbi_map = {}
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wbi_status_map = {}
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for r in cur.fetchall():
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wbi_map[r[0]] = r[1]
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wbi_status_map[r[0]] = r[2]
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# NCI
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cur.execute("""
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SELECT DISTINCT ON (member_id) member_id, display_score
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FROM dws.dws_member_newconv_index ORDER BY member_id, stat_date DESC
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""")
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nci_map = {r[0]: r[1] for r in cur.fetchall()}
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# 客户最后到店时间（最新结算时间）
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cur.execute("""
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SELECT member_id, MAX(pay_time)::date
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FROM dwd.dwd_settlement_head
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WHERE settle_type IN (1, 3) AND member_id > 0
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GROUP BY member_id
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""")
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last_visit_map = {r[0]: r[1] for r in cur.fetchall()}
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conn.close()
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return {
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"asst_names": asst_names, "asst_left": asst_left,
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"member_names": member_names, "member_mobiles": member_mobiles,
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"ri": ri_map,
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"wbi": wbi_map, "wbi_status": wbi_status_map,
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"nci": nci_map, "last_visit": last_visit_map,
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}
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def load_tasks(dsn: str, status_filter: list[str] | None = None) -> list[tuple]:
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conn = psycopg2.connect(dsn)
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conn.set_client_encoding("UTF8")
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cur = conn.cursor()
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sql = """
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SELECT id, assistant_id, member_id, task_type, status, priority_score,
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created_at::date, updated_at::date, completed_at::date, completion_type
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FROM biz.coach_tasks
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"""
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if status_filter:
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placeholders = ",".join(["%s"] * len(status_filter))
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sql += f" WHERE status IN ({placeholders})"
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cur.execute(sql, status_filter)
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else:
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cur.execute(sql)
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rows = cur.fetchall()
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conn.close()
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return rows
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# -- 报告生成 --
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def generate_report(etl: dict, tasks: list[tuple]) -> str:
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asst_names = etl["asst_names"]
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asst_left = etl["asst_left"]
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member_names = etl["member_names"]
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member_mobiles = etl["member_mobiles"]
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ri_map = etl["ri"]
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wbi_map = etl["wbi"]
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wbi_status = etl["wbi_status"]
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nci_map = etl["nci"]
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last_visit = etl["last_visit"]
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L = [] # 行缓冲
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L.append("# 助教业务任务明细报告")
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L.append("")
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L.append(f"> 导出日期：{date.today()}")
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L.append("")
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# -- 指标说明 --
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L.append("## 指标说明")
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L.append("")
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L.append("| 指标 | 全称 | 含义 | 范围 |")
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L.append("|------|------|------|------|")
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L.append("| **WBI** | 流失回赢指数 | 老客流失风险，越高越需要召回 | 0-10 |")
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L.append("| **NCI** | 新客转化指数 | 新客沉默风险，越高越需要跟进 | 0-10 |")
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L.append("| **RS** | 关系强度指数 | 助教与客户亲密度（频次+时长+近期性） | 0-10 |")
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L.append("| **OS** | 归属份额标签 | 客户主要由谁服务 | MAIN / COMANAGE / POOL |")
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L.append("| **MS** | 升温动量 | 近期频率 vs 长期频率 | 0-10 |")
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L.append("| **ML** | 付费关联 | 人工台账充值关联强度 | 0-10 |")
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L.append("")
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L.append("**OS**：MAIN=主服务(>=70%) | COMANAGE=共管(>=30%) | POOL=池内")
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L.append("")
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L.append("**任务判定**：`max(WBI,NCI)>7` -> 高优先召回 | `>5` -> 优先召回 | `1<RS<6` -> 关系构建 | 客户到店 -> 完成召回 -> 生成回访(48h)")
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L.append("")
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# -- 一、总览 --
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status_count = defaultdict(int)
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type_status = defaultdict(lambda: defaultdict(int))
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for t in tasks:
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status_count[t[4]] += 1
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type_status[t[3]][t[4]] += 1
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L.append("## 一、总览")
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L.append("")
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L.append("| 状态 | 数量 |")
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L.append("|------|------|")
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for s in ["active", "completed", "resolved", "expired", "inactive"]:
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if status_count[s]:
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L.append(f"| {STATUS_LABELS[s]} | {status_count[s]} |")
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L.append("")
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L.append("| 任务类型 | 进行中 | 已完成 | 已解除 | 已过期 | 已关闭 |")
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L.append("|----------|--------|--------|--------|--------|--------|")
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for tt in TYPE_ORDER:
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d = type_status[tt]
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L.append(f"| {TYPE_LABELS[tt]} | {d.get('active',0)} | {d.get('completed',0)} | {d.get('resolved',0)} | {d.get('expired',0)} | {d.get('inactive',0)} |")
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L.append("")
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# ── 二、助教任务详情（按助教 → 任务类型 → 状态排序） ──
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# 按助教分组
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by_asst: dict[int, list[tuple]] = defaultdict(list)
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for t in tasks:
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by_asst[t[1]].append(t)
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# 助教排序：在职优先，二级按任务数降序
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sorted_assts = sorted(
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by_asst.keys(),
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key=lambda a: (1 if a in asst_left else 0, -len(by_asst[a])),
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)
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L.append("## 二、助教任务详情")
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L.append("")
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for aid in sorted_assts:
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atasks = by_asst[aid]
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aname = asst_names.get(aid, str(aid))
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left_tag = "【已离职】" if aid in asst_left else "【在职】"
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active_n = sum(1 for t in atasks if t[4] == "active")
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completed_n = sum(1 for t in atasks if t[4] == "completed")
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L.append(f"### {aname}{left_tag}（进行中 {active_n} / 已完成 {completed_n} / 共 {len(atasks)}）")
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L.append("")
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# 按任务类型分组
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by_type: dict[str, list[tuple]] = defaultdict(list)
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for t in atasks:
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by_type[t[3]].append(t)
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for tt in TYPE_ORDER:
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tt_tasks = by_type.get(tt)
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if not tt_tasks:
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continue
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# 按状态排序：已完成 → 进行中 → 已过期
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tt_tasks.sort(key=lambda t: (STATUS_SORT.get(t[4], 9), -(t[5] or 0)))
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L.append(f"**{TYPE_LABELS[tt]}**（{len(tt_tasks)}）")
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L.append("")
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if tt in ("high_priority_recall", "priority_recall"):
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L.append("| 状态 | 客户 | 手机号 | 优先级 | WBI | NCI | RS | OS | 服务次数 | 末次距今 | 创建 | 更新 | 最后到店 |")
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L.append("|------|------|--------|--------|-----|-----|----|-----|----------|----------|------|------|----------|")
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elif tt == "follow_up_visit":
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L.append("| 状态 | 客户 | 手机号 | RS | OS | 服务次数 | 创建 | 更新 | 最后到店 | 完成/过期 |")
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L.append("|------|------|--------|----|-----|----------|------|------|----------|-----------|")
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else:
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L.append("| 状态 | 客户 | 手机号 | RS | OS | 服务次数 | 末次距今 | MS | ML | 创建 | 更新 | 最后到店 |")
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L.append("|------|------|--------|----|-----|----------|----------|-----|-----|------|------|----------|")
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for t in tt_tasks:
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tid, _, mid, _, status, prio, created, updated, completed_at, comp_type = t
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mname = member_names.get(mid, str(mid))
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mobile = fmt_mobile(member_mobiles.get(mid))
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st = STATUS_LABELS.get(status, status)
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ri = ri_map.get((aid, mid), {})
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wbi = wbi_map.get(mid)
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nci_raw = nci_map.get(mid)
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# 客户已转老客时，NCI 显示"老客"而非过时的历史高分
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is_old = wbi_status.get(mid) == "OLD"
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nci_str = "老客" if (is_old and nci_raw is not None) else fmt(nci_raw)
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lv = last_visit.get(mid)
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lv_str = str(lv) if lv else "-"
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if tt in ("high_priority_recall", "priority_recall"):
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L.append(
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f"| {st} | {mname} | {mobile} | {fmt(prio)} | {fmt(wbi)} | {nci_str}"
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f" | {fmt(ri.get('rs'))} | {ri.get('os_label', '-')} | {ri.get('sessions', '-')}"
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f" | {ri.get('days_since', '-')} | {created} | {updated} | {lv_str} |"
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)
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elif tt == "follow_up_visit":
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L.append(
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f"| {st} | {mname} | {mobile} | {fmt(ri.get('rs'))} | {ri.get('os_label', '-')}"
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f" | {ri.get('sessions', '-')} | {created} | {updated} | {lv_str} | {completed_at or '-'} |"
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)
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else:
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L.append(
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f"| {st} | {mname} | {mobile} | {fmt(ri.get('rs'))} | {ri.get('os_label', '-')}"
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f" | {ri.get('sessions', '-')} | {ri.get('days_since', '-')}"
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f" | {fmt(ri.get('ms'))} | {fmt(ri.get('ml'))} | {created} | {updated} | {lv_str} |"
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)
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L.append("")
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# ── 三、趋势分析 ──
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L.append("## 三、趋势分析")
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L.append("")
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# 3.1 月度趋势
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L.append("### 3.1 月度创建/完成/过期")
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L.append("")
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L.append("| 月份 | 创建 | 完成 | 过期 |")
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L.append("|------|------|------|------|")
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monthly = defaultdict(lambda: [0, 0, 0])
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for t in tasks:
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m = t[6].replace(day=1)
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monthly[m][0] += 1
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if t[4] == "completed":
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monthly[m][1] += 1
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if t[4] == "expired":
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monthly[m][2] += 1
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for m in sorted(monthly):
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c = monthly[m]
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L.append(f"| {m} | {c[0]} | {c[1]} | {c[2]} |")
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L.append("")
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# 3.2 召回完成率
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recall_tasks = [t for t in tasks if t[3] in ("high_priority_recall", "priority_recall")]
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if recall_tasks:
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total_recall = len(recall_tasks)
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completed_recall = sum(1 for t in recall_tasks if t[4] == "completed")
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rate = completed_recall / total_recall * 100 if total_recall else 0
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L.append("### 3.2 召回完成率")
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L.append("")
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L.append(f"- 召回任务总数：{total_recall}")
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L.append(f"- 已完成：{completed_recall}")
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L.append(f"- 完成率：**{rate:.1f}%**")
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L.append("")
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# 3.3 助教任务量排行
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L.append("### 3.3 助教任务量排行（进行中）")
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L.append("")
|
||||
L.append("| 排名 | 助教 | 高优先 | 优先 | 回访 | 关系 | 合计 |")
|
||||
L.append("|------|------|--------|------|------|------|------|")
|
||||
asst_active = defaultdict(lambda: defaultdict(int))
|
||||
for t in tasks:
|
||||
if t[4] == "active":
|
||||
asst_active[t[1]][t[3]] += 1
|
||||
rank = 1
|
||||
for aid in sorted(asst_active, key=lambda a: (1 if a in asst_left else 0, -sum(asst_active[a].values()))):
|
||||
d = asst_active[aid]
|
||||
aname = asst_names.get(aid, str(aid))
|
||||
left_tag = "【离职】" if aid in asst_left else ""
|
||||
total = sum(d.values())
|
||||
L.append(
|
||||
f"| {rank} | {aname}{left_tag} | {d.get('high_priority_recall',0)}"
|
||||
f" | {d.get('priority_recall',0)} | {d.get('follow_up_visit',0)}"
|
||||
f" | {d.get('relationship_building',0)} | {total} |"
|
||||
)
|
||||
rank += 1
|
||||
L.append("")
|
||||
|
||||
return "\n".join(L)
|
||||
|
||||
|
||||
# ── CLI ──
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="助教业务任务明细报告")
|
||||
parser.add_argument("--output", "-o", default=str(_ROOT / "docs" / "assistant-task-detail-report.md"))
|
||||
parser.add_argument("--status", default=None, help="状态过滤，逗号分隔")
|
||||
args = parser.parse_args()
|
||||
|
||||
pg_dsn = os.environ.get("PG_DSN")
|
||||
app_dsn = os.environ.get("APP_DB_DSN")
|
||||
if not pg_dsn or not app_dsn:
|
||||
print("错误：PG_DSN 和 APP_DB_DSN 必须在 .env 中配置", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
status_filter = [s.strip() for s in args.status.split(",")] if args.status else None
|
||||
etl_data = load_etl_data(pg_dsn)
|
||||
tasks = load_tasks(app_dsn, status_filter)
|
||||
report = generate_report(etl_data, tasks)
|
||||
|
||||
out_path = Path(args.output)
|
||||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
out_path.write_text(report, encoding="utf-8")
|
||||
print(f"Done. {report.count(chr(10))+1} lines -> {out_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user